Participants in our courses will learn to interpret large data sets quickly and reliably with modern multivariate data analysis. The courses include, in addition to theory, numerous demonstrations of examples and independent processing of real-life data in order to learn how the multivariate methods are applicable in practice. The exercises can also be carried out on own records with the help of the course leader.

For many years now, Multivariate Analysis (MVA) has been used by spectroscopists, analytical chemists, process engineers and sensory scientists to find important relationships in complex data tables. Methods such as Principal Component Analysis (PCA) have been used for data mining and exploratory data analysis purposes, while methods such as Partial Least Squares (PLS) have been used for predicting difficult to measure properties of products ranging from pharmaceuticals, petrochemical and agricultural comodities.

The Multivariate Analysis of Spectroscopic Data training is aimed at researchers and analysts who work with spectroscopic data, and are already familiar with fundamental multivariate methods such as PCA and PLS regression.

The courses have been designed for individuals who are involved in R&D, product development, process optimization, quality control & monitoring. Working with spectroscopic instruments (NIR, FTIR, UV, UV/VIS, NMR, DAS, Raman, Mass Spectroscopy) chromatography instruments (LC, GC, HPLC), production data, R&D, quality control or production processes.

The focus of the course will be on the application. Practical experiments serve to illustrate and underpin the theory. It is also shown that only an optimized measuring technique generates robust models with a problem-related chemometric evaluation.

The course is aimed at engineers, scientists and practitioners from all scientific disciplines who want to create calibration models using spectroscopic data.They want to replace the labor-intensive and hence expensive and lengthy standard analysis methods by means of fast spectroscopic measurement methods for process control